Overview

Dataset statistics

Number of variables26
Number of observations400
Missing cells1009
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.4 KiB
Average record size in memory208.3 B

Variable types

Numeric11
Categorical12
Boolean3

Alerts

wc has a high cardinality: 92 distinct values High cardinality
id is highly correlated with sg and 11 other fieldsHigh correlation
sg is highly correlated with id and 5 other fieldsHigh correlation
al is highly correlated with id and 14 other fieldsHigh correlation
su is highly correlated with bgr and 5 other fieldsHigh correlation
bgr is highly correlated with al and 5 other fieldsHigh correlation
bu is highly correlated with al and 9 other fieldsHigh correlation
sc is highly correlated with bu and 6 other fieldsHigh correlation
sod is highly correlated with pc and 6 other fieldsHigh correlation
hemo is highly correlated with id and 15 other fieldsHigh correlation
pc is highly correlated with id and 10 other fieldsHigh correlation
pcc is highly correlated with al and 1 other fieldsHigh correlation
rc is highly correlated with id and 19 other fieldsHigh correlation
classification is highly correlated with id and 6 other fieldsHigh correlation
dm is highly correlated with id and 5 other fieldsHigh correlation
htn is highly correlated with id and 12 other fieldsHigh correlation
ane is highly correlated with bu and 4 other fieldsHigh correlation
rbc is highly correlated with id and 6 other fieldsHigh correlation
pcv is highly correlated with id and 19 other fieldsHigh correlation
age is highly correlated with htnHigh correlation
bp is highly correlated with pcv and 1 other fieldsHigh correlation
ba is highly correlated with al and 1 other fieldsHigh correlation
pot is highly correlated with bu and 3 other fieldsHigh correlation
wc is highly correlated with id and 12 other fieldsHigh correlation
cad is highly correlated with su and 1 other fieldsHigh correlation
appet is highly correlated with id and 6 other fieldsHigh correlation
pe is highly correlated with al and 6 other fieldsHigh correlation
age has 9 (2.2%) missing values Missing
bp has 12 (3.0%) missing values Missing
sg has 47 (11.8%) missing values Missing
al has 46 (11.5%) missing values Missing
su has 49 (12.2%) missing values Missing
rbc has 152 (38.0%) missing values Missing
pc has 65 (16.2%) missing values Missing
bgr has 44 (11.0%) missing values Missing
bu has 19 (4.8%) missing values Missing
sc has 17 (4.2%) missing values Missing
sod has 87 (21.8%) missing values Missing
pot has 88 (22.0%) missing values Missing
hemo has 52 (13.0%) missing values Missing
pcv has 70 (17.5%) missing values Missing
wc has 105 (26.2%) missing values Missing
rc has 130 (32.5%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
al has 199 (49.8%) zeros Zeros
su has 290 (72.5%) zeros Zeros

Reproduction

Analysis started2022-09-24 15:56:22.354732
Analysis finished2022-09-24 15:56:36.383666
Duration14.03 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.5
Minimum0
Maximum399
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:36.447532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.95
Q199.75
median199.5
Q3299.25
95-th percentile379.05
Maximum399
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143013
Coefficient of variation (CV)0.5795203073
Kurtosis-1.2
Mean199.5
Median Absolute Deviation (MAD)100
Skewness0
Sum79800
Variance13366.66667
MonotonicityStrictly increasing
2022-09-24T21:26:36.539460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.2%
2631
 
0.2%
2731
 
0.2%
2721
 
0.2%
2711
 
0.2%
2701
 
0.2%
2691
 
0.2%
2681
 
0.2%
2671
 
0.2%
2661
 
0.2%
Other values (390)390
97.5%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
ValueCountFrequency (%)
3991
0.2%
3981
0.2%
3971
0.2%
3961
0.2%
3951
0.2%
3941
0.2%
3931
0.2%
3921
0.2%
3911
0.2%
3901
0.2%

age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.48337596
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:36.649339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.16971409
Coefficient of variation (CV)0.3335001594
Kurtosis0.0578404946
Mean51.48337596
Median Absolute Deviation (MAD)10
Skewness-0.6682594692
Sum20130
Variance294.7990819
MonotonicityNot monotonic
2022-09-24T21:26:36.748849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6019
 
4.8%
6517
 
4.2%
4812
 
3.0%
5012
 
3.0%
5512
 
3.0%
4711
 
2.8%
5610
 
2.5%
5910
 
2.5%
4510
 
2.5%
5410
 
2.5%
Other values (66)268
67.0%
ValueCountFrequency (%)
21
 
0.2%
31
 
0.2%
41
 
0.2%
52
0.5%
61
 
0.2%
71
 
0.2%
83
0.8%
111
 
0.2%
122
0.5%
141
 
0.2%
ValueCountFrequency (%)
901
 
0.2%
831
 
0.2%
821
 
0.2%
811
 
0.2%
804
1.0%
791
 
0.2%
781
 
0.2%
765
1.2%
755
1.2%
743
0.8%

bp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.46907216
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:36.813851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.68363749
Coefficient of variation (CV)0.1789434226
Kurtosis8.646095189
Mean76.46907216
Median Absolute Deviation (MAD)10
Skewness1.605428957
Sum29670
Variance187.2419351
MonotonicityNot monotonic
2022-09-24T21:26:36.872848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80116
29.0%
70112
28.0%
6071
17.8%
9053
13.2%
10025
 
6.2%
505
 
1.2%
1103
 
0.8%
1401
 
0.2%
1801
 
0.2%
1201
 
0.2%
(Missing)12
 
3.0%
ValueCountFrequency (%)
505
 
1.2%
6071
17.8%
70112
28.0%
80116
29.0%
9053
13.2%
10025
 
6.2%
1103
 
0.8%
1201
 
0.2%
1401
 
0.2%
1801
 
0.2%
ValueCountFrequency (%)
1801
 
0.2%
1401
 
0.2%
1201
 
0.2%
1103
 
0.8%
10025
 
6.2%
9053
13.2%
80116
29.0%
70112
28.0%
6071
17.8%
505
 
1.2%

sg
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size3.2 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.461756374
Min length4

Characters and Unicode

Total characters1575
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02106
26.5%
1.0184
21.0%
1.02581
20.2%
1.01575
18.8%
1.0057
 
1.8%
(Missing)47
11.8%

Length

2022-09-24T21:26:36.935848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:36.996987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02106
30.0%
1.0184
23.8%
1.02581
22.9%
1.01575
21.2%
1.0057
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1512
32.5%
0360
22.9%
.353
22.4%
2187
 
11.9%
5163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1222
77.6%
Other Punctuation353
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1512
41.9%
0360
29.5%
2187
 
15.3%
5163
 
13.3%
Other Punctuation
ValueCountFrequency (%)
.353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1512
32.5%
0360
22.9%
.353
22.4%
2187
 
11.9%
5163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1512
32.5%
0360
22.9%
.353
22.4%
2187
 
11.9%
5163
 
10.3%

al
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.016949153
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:37.075145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.352678913
Coefficient of variation (CV)1.330134264
Kurtosis-0.3833766021
Mean1.016949153
Median Absolute Deviation (MAD)0
Skewness0.9981572421
Sum360
Variance1.829740241
MonotonicityNot monotonic
2022-09-24T21:26:37.130206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0199
49.8%
144
 
11.0%
243
 
10.8%
343
 
10.8%
424
 
6.0%
51
 
0.2%
(Missing)46
 
11.5%
ValueCountFrequency (%)
0199
49.8%
144
 
11.0%
243
 
10.8%
343
 
10.8%
424
 
6.0%
51
 
0.2%
ValueCountFrequency (%)
51
 
0.2%
424
 
6.0%
343
 
10.8%
243
 
10.8%
144
 
11.0%
0199
49.8%

su
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.4501424501
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:37.183206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.099191252
Coefficient of variation (CV)2.441874237
Kurtosis5.055348003
Mean0.4501424501
Median Absolute Deviation (MAD)0
Skewness2.464261823
Sum158
Variance1.208221408
MonotonicityNot monotonic
2022-09-24T21:26:37.238206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0290
72.5%
218
 
4.5%
314
 
3.5%
413
 
3.2%
113
 
3.2%
53
 
0.8%
(Missing)49
 
12.2%
ValueCountFrequency (%)
0290
72.5%
113
 
3.2%
218
 
4.5%
314
 
3.5%
413
 
3.2%
53
 
0.8%
ValueCountFrequency (%)
53
 
0.8%
413
 
3.2%
314
 
3.5%
218
 
4.5%
113
 
3.2%
0290
72.5%

rbc
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size3.2 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.379032258
Min length6

Characters and Unicode

Total characters1582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal201
50.2%
abnormal47
 
11.8%
(Missing)152
38.0%

Length

2022-09-24T21:26:37.302207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:37.366533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal201
81.0%
abnormal47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1582
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a295
18.6%
n248
15.7%
o248
15.7%
r248
15.7%
m248
15.7%
l248
15.7%
b47
 
3.0%

pc
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size3.2 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.453731343
Min length6

Characters and Unicode

Total characters2162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal259
64.8%
abnormal76
 
19.0%
(Missing)65
 
16.2%

Length

2022-09-24T21:26:37.411174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:37.489286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal259
77.3%
abnormal76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a411
19.0%
n335
15.5%
o335
15.5%
r335
15.5%
m335
15.5%
l335
15.5%
b76
 
3.5%

pcc
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.2 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.681818182
Min length7

Characters and Unicode

Total characters3834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent354
88.5%
present42
 
10.5%
(Missing)4
 
1.0%

Length

2022-09-24T21:26:37.536114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:37.839025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
notpresent354
89.4%
present42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3834
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin3834
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e792
20.7%
n750
19.6%
t750
19.6%
p396
10.3%
r396
10.3%
s396
10.3%
o354
9.2%

ba
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.2 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.833333333
Min length7

Characters and Unicode

Total characters3894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent374
93.5%
present22
 
5.5%
(Missing)4
 
1.0%

Length

2022-09-24T21:26:37.901506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:37.966888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
notpresent374
94.4%
present22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin3894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e792
20.3%
n770
19.8%
t770
19.8%
p396
10.2%
r396
10.2%
s396
10.2%
o374
9.6%

bgr
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.0365169
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:38.029388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.28171424
Coefficient of variation (CV)0.5355551179
Kurtosis4.225593588
Mean148.0365169
Median Absolute Deviation (MAD)25
Skewness2.010773173
Sum52701
Variance6285.590212
MonotonicityNot monotonic
2022-09-24T21:26:38.107498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9910
 
2.5%
939
 
2.2%
1009
 
2.2%
1078
 
2.0%
1316
 
1.5%
1406
 
1.5%
1096
 
1.5%
926
 
1.5%
1176
 
1.5%
1306
 
1.5%
Other values (136)284
71.0%
(Missing)44
 
11.0%
ValueCountFrequency (%)
221
 
0.2%
705
1.2%
743
0.8%
752
 
0.5%
764
1.0%
783
0.8%
793
0.8%
802
 
0.5%
813
0.8%
823
0.8%
ValueCountFrequency (%)
4902
0.5%
4631
0.2%
4471
0.2%
4251
0.2%
4242
0.5%
4231
0.2%
4151
0.2%
4101
0.2%
3801
0.2%
3602
0.5%

bu
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.42572178
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:38.185617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.50300585
Coefficient of variation (CV)0.8794492133
Kurtosis9.345288576
Mean57.42572178
Median Absolute Deviation (MAD)16
Skewness2.634374459
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2022-09-24T21:26:38.269097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4615
 
3.8%
2513
 
3.2%
1911
 
2.8%
4010
 
2.5%
159
 
2.2%
489
 
2.2%
509
 
2.2%
189
 
2.2%
328
 
2.0%
498
 
2.0%
Other values (108)280
70.0%
(Missing)19
 
4.8%
ValueCountFrequency (%)
1.51
 
0.2%
102
 
0.5%
159
2.2%
167
1.8%
177
1.8%
189
2.2%
1911
2.8%
207
1.8%
211
 
0.2%
226
1.5%
ValueCountFrequency (%)
3911
0.2%
3221
0.2%
3091
0.2%
2411
0.2%
2351
0.2%
2231
0.2%
2191
0.2%
2171
0.2%
2151
0.2%
2081
0.2%

sc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.072454308
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:38.340101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.741126067
Coefficient of variation (CV)1.868579803
Kurtosis79.30434545
Mean3.072454308
Median Absolute Deviation (MAD)0.6
Skewness7.509538252
Sum1176.75
Variance32.96052852
MonotonicityNot monotonic
2022-09-24T21:26:38.418217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.240
 
10.0%
1.124
 
6.0%
0.523
 
5.8%
123
 
5.8%
0.922
 
5.5%
0.722
 
5.5%
0.618
 
4.5%
0.817
 
4.2%
2.210
 
2.5%
1.59
 
2.2%
Other values (74)175
43.8%
(Missing)17
 
4.2%
ValueCountFrequency (%)
0.41
 
0.2%
0.523
5.8%
0.618
4.5%
0.722
5.5%
0.817
4.2%
0.922
5.5%
123
5.8%
1.124
6.0%
1.240
10.0%
1.38
 
2.0%
ValueCountFrequency (%)
761
0.2%
48.11
0.2%
321
0.2%
241
0.2%
18.11
0.2%
181
0.2%
16.91
0.2%
16.41
0.2%
15.21
0.2%
151
0.2%

sod
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.528754
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:38.496395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.40875205
Coefficient of variation (CV)0.07568418785
Kurtosis85.53436962
Mean137.528754
Median Absolute Deviation (MAD)3
Skewness-6.996568561
Sum43046.5
Variance108.3421193
MonotonicityNot monotonic
2022-09-24T21:26:38.558905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
13540
10.0%
14025
 
6.2%
14122
 
5.5%
13921
 
5.2%
14220
 
5.0%
13820
 
5.0%
13719
 
4.8%
15017
 
4.2%
13617
 
4.2%
14713
 
3.2%
Other values (24)99
24.8%
(Missing)87
21.8%
ValueCountFrequency (%)
4.51
 
0.2%
1041
 
0.2%
1111
 
0.2%
1132
0.5%
1142
0.5%
1151
 
0.2%
1202
0.5%
1222
0.5%
1243
0.8%
1252
0.5%
ValueCountFrequency (%)
1631
 
0.2%
15017
4.2%
14713
3.2%
14610
 
2.5%
14511
2.8%
1449
 
2.2%
1434
 
1.0%
14220
5.0%
14122
5.5%
14025
6.2%

pot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.62724359
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:38.636987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.193904177
Coefficient of variation (CV)0.6902390407
Kurtosis142.5059115
Mean4.62724359
Median Absolute Deviation (MAD)0.5
Skewness11.58295556
Sum1443.7
Variance10.20102389
MonotonicityNot monotonic
2022-09-24T21:26:38.731314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.530
 
7.5%
530
 
7.5%
4.927
 
6.8%
4.717
 
4.2%
4.816
 
4.0%
414
 
3.5%
4.114
 
3.5%
4.414
 
3.5%
3.914
 
3.5%
3.814
 
3.5%
Other values (30)122
30.5%
(Missing)88
22.0%
ValueCountFrequency (%)
2.52
 
0.5%
2.71
 
0.2%
2.81
 
0.2%
2.93
 
0.8%
32
 
0.5%
3.23
 
0.8%
3.33
 
0.8%
3.45
 
1.2%
3.530
7.5%
3.68
 
2.0%
ValueCountFrequency (%)
471
 
0.2%
391
 
0.2%
7.61
 
0.2%
6.61
 
0.2%
6.52
0.5%
6.41
 
0.2%
6.33
0.8%
5.92
0.5%
5.82
0.5%
5.74
1.0%

hemo
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.52643678
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2022-09-24T21:26:38.823971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.912586609
Coefficient of variation (CV)0.2325151725
Kurtosis-0.4713980437
Mean12.52643678
Median Absolute Deviation (MAD)2.35
Skewness-0.3350946792
Sum4359.2
Variance8.483160754
MonotonicityNot monotonic
2022-09-24T21:26:38.902088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1516
 
4.0%
10.98
 
2.0%
13.67
 
1.8%
137
 
1.8%
9.87
 
1.8%
11.17
 
1.8%
10.36
 
1.5%
11.36
 
1.5%
13.96
 
1.5%
126
 
1.5%
Other values (105)272
68.0%
(Missing)52
 
13.0%
ValueCountFrequency (%)
3.11
0.2%
4.81
0.2%
5.51
0.2%
5.61
0.2%
5.81
0.2%
62
0.5%
6.11
0.2%
6.21
0.2%
6.31
0.2%
6.61
0.2%
ValueCountFrequency (%)
17.83
0.8%
17.71
 
0.2%
17.61
 
0.2%
17.51
 
0.2%
17.42
0.5%
17.31
 
0.2%
17.22
0.5%
17.12
0.5%
174
1.0%
16.92
0.5%

pcv
Categorical

HIGH CORRELATION
MISSING

Distinct44
Distinct (%)13.3%
Missing70
Missing (%)17.5%
Memory size3.2 KiB
52
 
21
41
 
21
48
 
19
44
 
19
40
 
16
Other values (39)
234 

Length

Max length3
Median length2
Mean length2
Min length1

Characters and Unicode

Total characters660
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)3.0%

Sample

1st row44
2nd row38
3rd row31
4th row32
5th row35

Common Values

ValueCountFrequency (%)
5221
 
5.2%
4121
 
5.2%
4819
 
4.8%
4419
 
4.8%
4016
 
4.0%
4314
 
3.5%
4213
 
3.2%
4513
 
3.2%
3612
 
3.0%
3312
 
3.0%
Other values (34)170
42.5%
(Missing)70
17.5%

Length

2022-09-24T21:26:38.984795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5221
 
6.4%
4121
 
6.4%
4819
 
5.8%
4419
 
5.8%
4016
 
4.8%
4315
 
4.5%
4213
 
3.9%
4513
 
3.9%
3212
 
3.6%
5012
 
3.6%
Other values (33)169
51.2%

Most occurring characters

ValueCountFrequency (%)
4175
26.5%
3129
19.5%
296
14.5%
571
10.8%
141
 
6.2%
038
 
5.8%
837
 
5.6%
628
 
4.2%
923
 
3.5%
719
 
2.9%
Other values (2)3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number657
99.5%
Control2
 
0.3%
Other Punctuation1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4175
26.6%
3129
19.6%
296
14.6%
571
10.8%
141
 
6.2%
038
 
5.8%
837
 
5.6%
628
 
4.3%
923
 
3.5%
719
 
2.9%
Control
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
?1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4175
26.5%
3129
19.5%
296
14.5%
571
10.8%
141
 
6.2%
038
 
5.8%
837
 
5.6%
628
 
4.2%
923
 
3.5%
719
 
2.9%
Other values (2)3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4175
26.5%
3129
19.5%
296
14.5%
571
10.8%
141
 
6.2%
038
 
5.8%
837
 
5.6%
628
 
4.2%
923
 
3.5%
719
 
2.9%
Other values (2)3
 
0.5%

wc
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct92
Distinct (%)31.2%
Missing105
Missing (%)26.2%
Memory size3.2 KiB
9800
 
11
6700
 
10
9600
 
9
7200
 
9
9200
 
9
Other values (87)
247 

Length

Max length5
Median length4
Mean length4.227118644
Min length2

Characters and Unicode

Total characters1247
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)11.5%

Sample

1st row7800
2nd row6000
3rd row7500
4th row6700
5th row7300

Common Values

ValueCountFrequency (%)
980011
 
2.8%
670010
 
2.5%
96009
 
2.2%
72009
 
2.2%
92009
 
2.2%
69008
 
2.0%
58008
 
2.0%
110008
 
2.0%
78007
 
1.8%
70007
 
1.8%
Other values (82)209
52.2%
(Missing)105
26.2%

Length

2022-09-24T21:26:39.047307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
980011
 
3.7%
670010
 
3.4%
96009
 
3.1%
72009
 
3.1%
92009
 
3.1%
69008
 
2.7%
58008
 
2.7%
110008
 
2.7%
78007
 
2.4%
70007
 
2.4%
Other values (80)209
70.8%

Most occurring characters

ValueCountFrequency (%)
0645
51.7%
199
 
7.9%
975
 
6.0%
675
 
6.0%
775
 
6.0%
867
 
5.4%
566
 
5.3%
255
 
4.4%
450
 
4.0%
336
 
2.9%
Other values (2)4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1243
99.7%
Control3
 
0.2%
Other Punctuation1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0645
51.9%
199
 
8.0%
975
 
6.0%
675
 
6.0%
775
 
6.0%
867
 
5.4%
566
 
5.3%
255
 
4.4%
450
 
4.0%
336
 
2.9%
Control
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
?1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0645
51.7%
199
 
7.9%
975
 
6.0%
675
 
6.0%
775
 
6.0%
867
 
5.4%
566
 
5.3%
255
 
4.4%
450
 
4.0%
336
 
2.9%
Other values (2)4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0645
51.7%
199
 
7.9%
975
 
6.0%
675
 
6.0%
775
 
6.0%
867
 
5.4%
566
 
5.3%
255
 
4.4%
450
 
4.0%
336
 
2.9%
Other values (2)4
 
0.3%

rc
Categorical

HIGH CORRELATION
MISSING

Distinct46
Distinct (%)17.0%
Missing130
Missing (%)32.5%
Memory size3.2 KiB
5.2
 
18
4.5
 
16
4.9
 
14
4.7
 
11
3.9
 
10
Other values (41)
201 

Length

Max length3
Median length3
Mean length2.818518519
Min length1

Characters and Unicode

Total characters761
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.5%

Sample

1st row5.2
2nd row3.9
3rd row4.6
4th row4.4
5th row5

Common Values

ValueCountFrequency (%)
5.218
 
4.5%
4.516
 
4.0%
4.914
 
3.5%
4.711
 
2.8%
3.910
 
2.5%
510
 
2.5%
4.810
 
2.5%
4.69
 
2.2%
3.49
 
2.2%
5.98
 
2.0%
Other values (36)155
38.8%
(Missing)130
32.5%

Length

2022-09-24T21:26:39.109792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.218
 
6.7%
4.516
 
5.9%
4.914
 
5.2%
4.711
 
4.1%
3.910
 
3.7%
510
 
3.7%
4.810
 
3.7%
4.69
 
3.3%
3.49
 
3.3%
6.18
 
3.0%
Other values (36)155
57.4%

Most occurring characters

ValueCountFrequency (%)
.245
32.2%
5115
15.1%
4115
15.1%
375
 
9.9%
652
 
6.8%
248
 
6.3%
934
 
4.5%
827
 
3.5%
726
 
3.4%
122
 
2.9%
Other values (2)2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number514
67.5%
Other Punctuation246
32.3%
Control1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5115
22.4%
4115
22.4%
375
14.6%
652
10.1%
248
9.3%
934
 
6.6%
827
 
5.3%
726
 
5.1%
122
 
4.3%
Other Punctuation
ValueCountFrequency (%)
.245
99.6%
?1
 
0.4%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common761
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.245
32.2%
5115
15.1%
4115
15.1%
375
 
9.9%
652
 
6.8%
248
 
6.3%
934
 
4.5%
827
 
3.5%
726
 
3.4%
122
 
2.9%
Other values (2)2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.245
32.2%
5115
15.1%
4115
15.1%
375
 
9.9%
652
 
6.8%
248
 
6.3%
934
 
4.5%
827
 
3.5%
726
 
3.4%
122
 
2.9%
Other values (2)2
 
0.3%

htn
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False251
62.7%
True147
36.8%
(Missing)2
 
0.5%
2022-09-24T21:26:39.189123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

dm
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.3%
Missing2
Missing (%)0.5%
Memory size3.2 KiB
no
258 
yes
134 
no
 
3
yes
 
2
yes
 
1

Length

Max length4
Median length2
Mean length2.359296482
Min length2

Characters and Unicode

Total characters939
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowyes
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no258
64.5%
yes134
33.5%
no3
 
0.8%
yes2
 
0.5%
yes1
 
0.2%
(Missing)2
 
0.5%

Length

2022-09-24T21:26:39.251614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:39.345202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no261
65.6%
yes137
34.4%

Most occurring characters

ValueCountFrequency (%)
n261
27.8%
o261
27.8%
y137
14.6%
e137
14.6%
s137
14.6%
5
 
0.5%
1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter933
99.4%
Control5
 
0.5%
Space Separator1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n261
28.0%
o261
28.0%
y137
14.7%
e137
14.7%
s137
14.7%
Control
ValueCountFrequency (%)
5
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin933
99.4%
Common6
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n261
28.0%
o261
28.0%
y137
14.7%
e137
14.7%
s137
14.7%
Common
ValueCountFrequency (%)
5
83.3%
1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n261
27.8%
o261
27.8%
y137
14.6%
e137
14.6%
s137
14.6%
5
 
0.5%
1
 
0.1%

cad
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.8%
Missing2
Missing (%)0.5%
Memory size3.2 KiB
no
362 
yes
 
34
no
 
2

Length

Max length3
Median length2
Mean length2.090452261
Min length2

Characters and Unicode

Total characters832
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no362
90.5%
yes34
 
8.5%
no2
 
0.5%
(Missing)2
 
0.5%

Length

2022-09-24T21:26:39.407747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:39.485824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no364
91.5%
yes34
 
8.5%

Most occurring characters

ValueCountFrequency (%)
n364
43.8%
o364
43.8%
y34
 
4.1%
e34
 
4.1%
s34
 
4.1%
2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter830
99.8%
Control2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n364
43.9%
o364
43.9%
y34
 
4.1%
e34
 
4.1%
s34
 
4.1%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin830
99.8%
Common2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n364
43.9%
o364
43.9%
y34
 
4.1%
e34
 
4.1%
s34
 
4.1%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n364
43.8%
o364
43.8%
y34
 
4.1%
e34
 
4.1%
s34
 
4.1%
2
 
0.2%

appet
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.2 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good317
79.2%
poor82
 
20.5%
(Missing)1
 
0.2%

Length

2022-09-24T21:26:39.532750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:39.605249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
good317
79.4%
poor82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1596
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1596
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o798
50.0%
g317
 
19.9%
d317
 
19.9%
p82
 
5.1%
r82
 
5.1%

pe
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False323
80.8%
True76
 
19.0%
(Missing)1
 
0.2%
2022-09-24T21:26:39.669476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

ane
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False339
84.8%
True60
 
15.0%
(Missing)1
 
0.2%
2022-09-24T21:26:39.742697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

classification
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
ckd
248 
notckd
150 
ckd
 
2

Length

Max length6
Median length3
Mean length4.13
Min length3

Characters and Unicode

Total characters1652
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowckd
2nd rowckd
3rd rowckd
4th rowckd
5th rowckd

Common Values

ValueCountFrequency (%)
ckd248
62.0%
notckd150
37.5%
ckd 2
 
0.5%

Length

2022-09-24T21:26:39.792594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-24T21:26:39.855091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ckd250
62.5%
notckd150
37.5%

Most occurring characters

ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%
2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1650
99.9%
Control2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1650
99.9%
Common2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c400
24.2%
k400
24.2%
d400
24.2%
n150
 
9.1%
o150
 
9.1%
t150
 
9.1%
2
 
0.1%

Interactions

2022-09-24T21:26:34.599493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:26.563099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.394588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.109064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.960930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.783633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.498094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.427696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.213803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.978806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.752732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.676279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:26.648521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.459334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.311147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.026763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.859020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.595518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.520227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.295222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.065320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.815976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.739196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:26.716650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.515216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.384902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.091636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.918966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.682459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.584205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.355466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.124322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.869979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.813239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:26.794215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.604398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.449479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.162850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.990421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.775459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.650366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.429046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.200001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.086526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.874764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:26.854934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.664396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.512543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.257485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.051406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.976297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.728120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.518677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.257684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.146240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.942735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:26.918025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.726048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.580988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.349485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.116738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.054743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.804451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.590101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.324411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.208423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:35.009499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.005411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.796992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.640990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.423138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.173742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.102109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.870501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.656990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.384738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.266420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:35.081505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.066408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.865988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.693964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.516604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.237532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.179412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.943526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.726343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.456464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.335694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:35.137944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.151099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.927030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.771490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.593267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.299533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.241210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.011600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.793427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.549659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.402784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:35.216493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.230627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.987262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.836236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.654268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.364931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.300353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.091302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.862541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.620324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.462554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:35.276955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:27.300981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.040264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:28.893392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:29.709270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:30.423611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:31.356866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.153630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:32.921137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:33.685348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T21:26:34.541466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-24T21:26:39.933250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-24T21:26:40.018911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-24T21:26:40.120173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-24T21:26:40.198292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-24T21:26:40.318506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-24T21:26:35.420447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-24T21:26:35.858908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-24T21:26:36.062758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-24T21:26:36.307920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
0048.080.01.0201.00.0NaNnormalnotpresentnotpresent121.036.01.2NaNNaN15.44478005.2yesyesnogoodnonockd
117.050.01.0204.00.0NaNnormalnotpresentnotpresentNaN18.00.8NaNNaN11.3386000NaNnononogoodnonockd
2262.080.01.0102.03.0normalnormalnotpresentnotpresent423.053.01.8NaNNaN9.6317500NaNnoyesnopoornoyesckd
3348.070.01.0054.00.0normalabnormalpresentnotpresent117.056.03.8111.02.511.23267003.9yesnonopooryesyesckd
4451.080.01.0102.00.0normalnormalnotpresentnotpresent106.026.01.4NaNNaN11.63573004.6nononogoodnonockd
5560.090.01.0153.00.0NaNNaNnotpresentnotpresent74.025.01.1142.03.212.23978004.4yesyesnogoodyesnockd
6668.070.01.0100.00.0NaNnormalnotpresentnotpresent100.054.024.0104.04.012.436NaNNaNnononogoodnonockd
7724.0NaN1.0152.04.0normalabnormalnotpresentnotpresent410.031.01.1NaNNaN12.44469005noyesnogoodyesnockd
8852.0100.01.0153.00.0normalabnormalpresentnotpresent138.060.01.9NaNNaN10.83396004yesyesnogoodnoyesckd
9953.090.01.0202.00.0abnormalabnormalpresentnotpresent70.0107.07.2114.03.79.529121003.7yesyesnopoornoyesckd

Last rows

idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
39039052.080.01.0250.00.0normalnormalnotpresentnotpresent99.025.00.8135.03.715.05263005.3nononogoodnononotckd
39139136.080.01.0250.00.0normalnormalnotpresentnotpresent85.016.01.1142.04.115.64458006.3nononogoodnononotckd
39239257.080.01.0200.00.0normalnormalnotpresentnotpresent133.048.01.2147.04.314.84666005.5nononogoodnononotckd
39339343.060.01.0250.00.0normalnormalnotpresentnotpresent117.045.00.7141.04.413.05474005.4nononogoodnononotckd
39439450.080.01.0200.00.0normalnormalnotpresentnotpresent137.046.00.8139.05.014.14595004.6nononogoodnononotckd
39539555.080.01.0200.00.0normalnormalnotpresentnotpresent140.049.00.5150.04.915.74767004.9nononogoodnononotckd
39639642.070.01.0250.00.0normalnormalnotpresentnotpresent75.031.01.2141.03.516.55478006.2nononogoodnononotckd
39739712.080.01.0200.00.0normalnormalnotpresentnotpresent100.026.00.6137.04.415.84966005.4nononogoodnononotckd
39839817.060.01.0250.00.0normalnormalnotpresentnotpresent114.050.01.0135.04.914.25172005.9nononogoodnononotckd
39939958.080.01.0250.00.0normalnormalnotpresentnotpresent131.018.01.1141.03.515.85368006.1nononogoodnononotckd